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Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for
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Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications Paperback - 2012 - 1st Edition

by James Pustejovsky; Amber Stubbs

Details

  • Title Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
  • Author James Pustejovsky; Amber Stubbs
  • Binding Paperback
  • Edition number 1st
  • Edition 1
  • Pages 339
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media, n
  • Date 2012-12-04
  • Illustrated Yes
  • Features Annotated, Bibliography, Illustrated, Index
  • ISBN 9781449306663 / 1449306667
  • Weight 1.21 lbs (0.55 kg)
  • Dimensions 9.01 x 7.07 x 0.73 in (22.89 x 17.96 x 1.85 cm)
  • Library of Congress subjects Machine learning, Natural language processing (Computer
  • Library of Congress Catalog Number 2012289691
  • Dewey Decimal Code 006.35

About the author

James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. His main areas of interest include: lexical meaning, computational semantics, temporal and spatial reasoning, and corpus linguistics. He is active in the development of standards for interoperability between language processing applications, and lead the creation of the recently adopted ISO standard for time annotation, ISO-TimeML. He is currently heading the development of a standard for annotating spatial information in language. More information on publications and research activities can be found at his webpage: pusto.com.

Amber Stubbs recently completed her Ph.D. in Computer Science at Brandeis University, and is currently a Postdoctoral Associate at SUNY Albany. Her dissertation focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Her website can be found at http: //pages.cs.brandeis.edu/ astubbs/

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